An average person spends two hours a day on social media, but almost half of us eat lunch alone. We created Connect to solve that problem. Since its creation in 2015, we have arranged nearly 10,000 platonic, in-person connections between students, alumni and staff at MIT, and are expanding to other schools in the United States, and abroad. Contact us if you're interested in bringing Connect to your University.

What you say and how you say it can teach us a lot about you! We are developing algorithms that passively learn about physical and emotional health using speech, text, and physiological signals. We've developed methods to detect cognitive impairment, and how people feel during conversations.

Decisions about patient care, especially in the ICU, are complicated. Doctors consider an incredible number of factors when deciding what exams to order, and what treatments to prescribe. But doctors aren't machines; In addition to signals, charts, and measurements, they also have "gut feelings" about their patients. We want to understand if these gut feelings are measurable, and to what extent they impact care decisions. To answer these questions we've studied 10 years of medical data, collecting information on all the patient factors that influence doctor's care decisions, but we also quantified how the doctors felt about their patients by performing sentiment analysis on their notes. We discovered that a doctor's feelings about a patient predicted care decisions, independent of what the hard data was telling them.

There's nothing as refreshing as a good night's sleep and few things compare to the discomfort of sleep deprivation. Everyone sleeps, but exactly why we sleep,
and how it affects our waking lives, remains a mystery. We are collaborating with clinicians to better understand why we sleep, and releasing data so others can join us
in the search for answers!

Fake news is a big problem, but it's not immediately obvious what makes a story false. We're learning how rumors are generated,
and tracking their propagation through social network so they can be stopped!

Cardiac arrest impacts over half a million people a year. Even if successfully resuscitated, patients can enter an indefinite coma. Predicting if patients will wake up from coma can prevent premature withdraw of care. We compiled the world's largest dataset of Electroencephalograms (EEG) from patients in coma after cardiac arrest, and built advanced algorithms that use EEG to rapidly predict coma outcomes.

Mis-dosing sensitive medications can have dire consequences for patient care. We developed a personalized medication dosing algorithm that is robust to missing data, a common problem in intensive care settings. The approach was 29% more accurate than intensive care staff, and better able to distinguish outcomes than non-personalized models.

Clinical researchers, historians, educators and field researchers regularly capture data on paper spreadsheets. We built a tool that transcribes images of paper-based spreadsheets into electronic form. The open-source tool provides a generalized solution for spreadsheet transcription that maintains privacy, is up to 10 times faster and twice as cost effective as existing alternatives.

Genetic factors contribute to the etiology of mental diseases, including schizophrenia. We developed a method to identify genetic variations associated with functional brain networks in schizophrenia. We found functional networks located in the thalamus, anterior and posterior cingulate gyri. The contributing genetic factors fell into two clusters centered at chromosome 7q21 and chromosome 5q35.

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. We developed a novel method to extract these complex representations through simple tasks. The extracted distributions allow us to predict the behavior of subjects to novel stimuli.